### g:SCS algorithm

**g:SCS method** is the default method for computing multiple testing correction for p-values gained from GO and pathway enrichment analysis. It corresponds to an experiment-wide threshold of

*a=0.05*, i.e. at least

*95%* of matches above threshold are statistically significant.

This approach is based on the idea that standard multiple testing corrections such as

**Bonferroni correction** and

**Benjamini-Hochberg False Discovery rate** are designed for multiple tests that are independent of each other. This is not correct for the analysis in g:GOSt, since GO consists of hierarchically related general and specific terms. The

**True Path Rule** of GO states that genes associated to a given go term

**t** are implicitly associated to all more general parents of term

**t**.

g:SCS threshold is a value pre-calculated for query list sizes up to 1000 genes. Given a fixed input query size, g:SCS analytically approximates a threshold

*t* corresponding to the 5% upper quantile of randomly generated queries of that size. All actual p-values resulting from the query are transformed to corrected p-values by multiplying these to the ratio of the approximate threshold

*t* and the initial experiment-wide threshold

*a=0.05*.

The algorithm considers the set structure underlying gene sets annotated to terms of each organism, and should therefore give a tighter threshold to significant results. g:SCS thresholds perfectly agreed in simulations with randomly generated gene sets of fixed input query sizes.